Are Magnetic Resonance Imaging Technologies Crucial to Our Understanding of Spinal Conditions?
Bibliographic record
Abstract
Synopsis Persistent spinal (traumatic and nontraumatic) pain is common and contributes to high societal and personal costs globally. There is an acknowledged urgency for new and interdisciplinary approaches to the condition, and soft tissues, including skeletal muscles, the spinal cord, and the brain, are rightly receiving increased attention as important biological contributors. In reaction to the recent suspicion and questioned value of imaging-based findings, this paper serves to recognize the promise that the technological evolution of imaging techniques, and particularly magnetic resonance imaging, is allowing in characterizing previously less visible morphology. We emphasize the value of quantification and data analysis of several contributors in the biopsychosocial model for understanding spinal pain. Further, we highlight emerging evidence regarding the pathobiology of changes to muscle composition (eg, atrophy, fatty infiltration), as well as advancements in neuroimaging and musculoskeletal imaging techniques (eg, fat-water imaging, functional magnetic resonance imaging, diffusion imaging, magnetization transfer imaging) for these important soft tissues. These noninvasive and objective data sources may complement known prognostic factors of poor recovery, patient self-report, diagnostic tests, and the “-omics” fields. When combined, advanced “big-data” analyses may assist in identifying associations previously not considered. Our clinical commentary is supported by empirical findings that may orient future efforts toward collaborative conversation, hypothesis generation, interdisciplinary research, and translation across a number of health fields. Our emphasis is that magnetic resonance imaging technologies and research are crucial to the advancement of our understanding of the complexities of spinal conditions. J Orthop Sports Phys Ther 2019;49(5):320–329. Epub 26 Mar 2019. doi:10.2519/jospt.2019.8793
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.014 | 0.027 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".